Electronic Disease Surveillance Monitoring Network (DiSMoN): Using
self-recorded data as a source of information to detect the spread of
contagious disease
Assume that you are the mayor in a large city, and ask “the mirror” in the morning: How are
we today? (“we” as for the whole population in your city). “The mirror” then visualizes the
health status for the citizens based on data from EDMoN, an Electronic Disease Surveillance
Monitoring Network. EDMoN provides information about the spread of contagious diseases,
air quality, pollution, and other factors affecting peoples’ health, particularly people suffering
from noncommunicable diseases. The information is made available to all citizens. If you
suffer from COPD, you need to know whether the air quality in your neighborhood is good or
not. If you are a person with diabetes, you want to know whether the risk of infections has
increased, and if possible, in which areas of the city. Such information is important for all
people with weakened immune system or poor health and can be provided by EDMoN.
EDMoN will use techniques from big data analytics, social media, mobile computing and a
novel health-monitoring system. The first version of EDMoN will use people with diabetes to
monitor contagious diseases.
To our knowledge, disease surveillance systems for the detection of a contagious disease at
a very early stage, i.e., within hours after the first persons in a population have been infected,
do not exist. Examples like Google Flu or other systems based on real-time data have not been
designed to support such activities and they are mainly designed to detect an epidemic.
Healthmap1 is one other example of software that mines websites, social networks and local
news reports to map potential disease outbreaks — still based on data following the onset of
the first symptoms.
In certain cases, EDMoN might be able to issue a warning 1-2 weeks ahead of the earliest point
of detection by today’s systems. For many patient groups as well as for health professionals,
this information is highly appreciated. To be successful, systems like EDMoN require (1) access
to a large cohort of people who frequently and regularly record their own health data and
make them available for secondary use, (2) good understanding of the complex physiological
nature of the human body and the different effects caused by pathogens, and (3)
computational models for the identification of deviations from expected values. If successful,
our project will form the basis for a new direction in the area of disease surveillance.
Currently, a number of disease surveillance and control programs are used to identify
potential disease outbreaks and other biological threats. Surveillance, as it was articulated in
the 2005 International Health Regulations (IHA 2005) is ‘the systematic ongoing collection and
analysis of data for public health purposes and the timely dissemination of public health
information for assessment and public health response as necessary’2.
A common characteristic of the disease surveillance approaches is that they mainly target
the general population. Less attention is paid to groups with special physical needs that are
rather vulnerable to infections (could be defined here as Sensitive Population Groups - SPGs),
such as people suffering from COPD or diabetes, who may be at heightened risk even in non-
1
http://www.emergencymgmt.com/health/Social-Media-Data-Identify-Outbreaks.html
WHO. Revision of the International Health Regulations: WHA 58.3 2005. Available from:
http://www.who.int/gb/ebwha/pdf_files/WHA58-REC1/english/Resolutions.pdf
2
outbreak settings3 and can easily spread an infection under certain circumstances4 Higgs et
al.5 described a system for the early detection of tuberculosis outbreaks among the San
Francisco homeless population (that could be also considered vulnerable due to social
reasons). To our knowledge, the Electronic Disease Surveillance Monitoring Network for SPGs
(as they are defined here) has not been described before and we have been the first to raise
this issue6. We have particularly discussed the disease surveillance for diabetics and have
published an analysis, which showed that the blood glucose level increased significantly after
infection in type-1 diabetics who were under tight monitoring7. Moreover, we have done and
published a preliminary study towards modeling of an infection related blood glucose
deviation detection algorithm8. In this context, the EDMoN project respects the principles of
the European Parliament Written Declaration on Diabetes9,10.
In the ‘Electronic Disease Surveillance Monitoring Network (EDMoN)’ project we will address
the following objectives: (O1) Establish an appropriate distributed system architecture for an
Electronic Disease Surveillance Monitoring Network that incorporates health-related data
from people with chronic and noncommunicable diseases (i.e., diabetes in the first version);
(O2) Identify appropriate equipment and develop modules to collect accurate physiological
and physical data from people with chronic and noncommunicable diseases (i.e., diabetes:
blood glucose values, infection-related data, body thermometers, insulin and food intake,
physical activity); (O3) Develop dedicated mathematical models that will process the incoming
data to facilitate the early detection of infections, i.e., for some people, before the onset of
the first symptoms; and, (O4) Produce notifications or alerts according to the processing
output from the Electronic Disease Surveillance Monitoring Network.
In the EDMoN system, the data collection will be accomplished through the use of small
medical devices, e.g., blood glucose monitors, body thermometers, physical activity sensors
(as FitBit, Jawbone, Garmin, Misfit), etc., which will measure and automatically transmit
physiology data to the receptor. A smart phone with the dedicated software will be the
receptor. The recorded data will then be transmitted to a cloud-based service for further
processing. The incoming data will feed dedicated mathematical models that will analyze the
input and produce alerts when an anomaly is detected. Thus, our project besides its ICT value
will have a clear benefit for society. It should also be mentioned here that data security issues
and privacy protection will be ensured in all system processes and will agree with the National
and International Ethical Guidelines.
3
Botsis, T., J.G. Bellika, and G. Hartvigsen, Disease surveillance systems for sensitive population groups. Advances in Disease
Surveillance, 2007. 4: p. 148.
4
Baker, E.H., et al. Hyperglycemia and pulmonary infection. in Nutricion Society. 2006
5
Higgs, B.W., et al., Early detection of tuberculosis outbreaks among the San Francisco homeless: trade-offs between spatial
resolution and temporal scale. PLoS ONE, 2007. 2(12): p. e1284.
6
Botsis, T., et al., Blood glucose levels as an indicator for the early detection of infections in type-1 diabetics. Advances in
Disease Surveillance, 2007. 4: p. 147.
7
Botsis, T., et al., Electronic disease surveillance for sensitive population groups - the diabetics case study. Stud Health
Technol Inform, 2008. 136: p. 365-70.
8
A.Z. Woldaregay, K. van Vuurden, E. Årsand, T. Botsis, and G. Hartvigsen, Electronic Disease Surveillance System Based
on Input from People with Diabetes: An Early Outbreak Detection Mechanism, in: Proceedings from The 14th Scandinavian
Conference on Health Informatics 2016, Gothenburg, Sweden, April 6-7 2016, Linköping University Electronic Press, 2016,
pp. 23-27
9 This declaration states among others that 1) diabetes should be prioritized in the EU’s health strategy and 2) the Member
States should be encouraged to establish national diabetes plans and work on relative strategies in many levels.
10
D. Cyr, Modeling web site design across cultures: Relationships to trust, satisfaction, and e-loyalty, Journal of
Management Information Systems 24 (2008), 47-72.